Abstract: This study investigates the convergence of photonic quantum computing and quantum topological data analysis as an emerging DeepTech pathway for HealthTech, ClimateTech and FinTech. The paper develops a structured literature-review and mathematical framework that connects photonic hardware assumptions, persistent homology, quantum algorithms for topological invariants and commercialization-readiness assessment. The central problem is not whether quantum computing will replace classical analytics in the immediate term, but how a defensible hybrid architecture can identify tasks where topology, optical quantum information processing and sector-specific data geometry create plausible advantage. The research therefore distinguishes near-term value from classical topological machine learning, medium-term value from quantum algorithmic benchmarking and long-term value from fault-tolerant photonic architectures. The method combines documentary evidence from official quantum strategies and peer-reviewed technical literature with a source-calibrated indicator matrix. Public data include national quantum programme commitments and reported photonic quantum projects, while coded sector indicators represent data intensity, photonic fit, QTDA relevance, infrastructure readiness, regulatory maturity, commercial pull and governance burden. Mathematical support is provided through equations for quantum state representation, Vietoris-Rips filtrations, persistent Betti numbers, persistence landscapes and a weighted readiness index. Results indicate that FinTech shows the strongest near-term operational readiness for topological early-warning systems, HealthTech offers the deepest scientific and clinical transformation potential, and ClimateTech provides the clearest public-value use case through explainable anomaly detection, carbon monitoring and resilience planning. The paper concludes that commercialization should proceed through staged validation, with classical TDA as the immediate baseline, quantum algorithms as verifiable benchmark objects and photonic quantum computing as a strategic, fault-tolerant horizon. Keywords: photonic quantum computing; quantum topological data analysis; persistent homology; DeepTech commercialization; HealthTech; ClimateTech; FinTech; quantum algorithms; topological machine learning; innovation governance.
Abstract: This study investigates the convergence of photonic quantum computing and quantum topological data analysis as an emerging DeepTech pathway for HealthTech, ClimateTech and FinTech. The paper develops a structured literature-review and mathematical framework that connects photonic hardware assumptions, persistent homology, quantum algorithms for topological invariants and commercialization-readiness assessment. The central problem is not whether quantum computing will replace classical analytics in the immediate term, but how a defensible hybrid architecture can identify tasks where topology, optical quantum information processing and sector-specific data geometry create plausible advantage. The research therefore distinguishes near-term value from classical topological machine learning, medium-term value from quantum algorithmic benchmarking and long-term value from fault-tolerant photonic architectures. The method combines documentary evidence from official quantum strategies and peer-reviewed technical literature with a source-calibrated indicator matrix. Public data include national quantum programme commitments and reported photonic quantum projects, while coded sector indicators represent data intensity, photonic fit, QTDA relevance, infrastructure readiness, regulatory maturity, commercial pull and governance burden. Mathematical support is provided through equations for quantum state representation, Vietoris-Rips filtrations, persistent Betti numbers, persistence landscapes and a weighted readiness index. Results indicate that FinTech shows the strongest near-term operational readiness for topological early-warning systems, HealthTech offers the deepest scientific and clinical transformation potential, and ClimateTech provides the clearest public-value use case through explainable anomaly detection, carbon monitoring and resilience planning. The paper concludes that commercialization should proceed through staged validation, with classical TDA as the immediate baseline, quantum algorithms as verifiable benchmark objects and photonic quantum computing as a strategic, fault-tolerant horizon. Keywords: photonic quantum computing; quantum topological data analysis; persistent homology; DeepTech commercialization; HealthTech; ClimateTech; FinTech; quantum algorithms; topological machine learning; innovation governance.
Abstract: Quantum Topological Data Analysis (QTDA) is introduced as a hybrid classical–quantum methodology for accelerating persistent homology on complex manifolds while preserving topological interpretability. The approach encodes simplicial complexes into quantum states, applies boundary operators via unitary transforms, and estimates invariants with variational and linear‑algebraic quantum routines. An end‑to‑end pipeline is formalized that couples deep representation learning with quantum‑assisted homological primitives, yielding stable persistence diagrams aligned with structural robustness. Experiments on synthetic and semi‑synthetic filtrations quantify latency, scaling, and stability, showing empirical reductions in runtime growth under modest qubit budgets. The results indicate that QTDA enables time‑critical analytics such as anomaly detection and manifold clustering, while meeting explainability and governance requirements through auditable topological summaries. Keywords Quantum Topological Data Analysis, persistent homology, Betti numbers, simplicial complexes, variational quantum algorithms, manifold learning, interpretability, auditability
Abstract: Biomedical intelligence increasingly depends on integrating data that are heterogeneous not only in format but also in biological scale: dynamic brain networks, high-frequency physiological signals, spatially resolved molecular measurements and treatment-response trajectories. This paper develops a unified conceptual and mathematical architecture—Quantum–Photonic Topological Intelligence for Multiscale Biomedicine (QPTI-MB)—for representing, fusing and governing these data without reducing clinically important structure to a single flat feature space. The proposed framework combines graph and simplicial representations, persistent topological descriptors, multimodal learning, hybrid quantum–photonic feature maps and constrained therapeutic optimization. The methodology is model-based. No patient-level dataset is supplied; therefore, the study uses an explicitly illustrative 0–10 numerical scoring model and does not claim empirical clinical superiority. The framework formalizes four coupled layers: topology-preserving biomedical encoding, quantum–photonic representation, uncertainty-aware cross-modal fusion and benefit–risk–cost constrained therapeutic decision support. A risk-adjusted composite score demonstrates how technical capability can be discounted by uncertainty, bias and translational risk. The analysis indicates that the value of the architecture lies less in any single computational technology than in disciplined multiscale coupling: persistent representations can stabilize structural information across sampling regimes; multimodal fusion can connect electrophysiology with spatial molecular states; and governed optimization can transform predictions into constrained therapeutic recommendations. The paper contributes a named framework, equations, variables, validation logic, scenario analysis and a translational roadmap. Future empirical work should benchmark classical, topological, quantum-inspired and quantum–photonic components under matched datasets, prospective validation, fairness constraints and clinically meaningful endpoints before any claim of deployment advantage is made. Keywords: quantum photonics; topological data analysis; persistent homology; brain connectivity; EEG; ECG; spatial multi-omics; precision oncology; multimodal learning; quantum neural networks; biomedical signal intelligence; uncertainty quantification; autonomous therapeutics; precision medicine; explainable clinical AI
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
Abstract Quantum technologies — encompassing quantum sensing, quantum computing and machine learning (QML), quantum nanomaterials, and quantum cryptography — represent an emerging class of physical and computational tools with the potential to address fundamental limitations in clinical medicine across diagnostics, therapeutics, drug discovery, and health-information security. Despite a rapidly growing body of peer-reviewed evidence, estimated at a compound annual growth rate of approximately 14.8% over 2016–2025, the extant literature organises findings by technology type rather than clinical specialty, limiting accessibility for clinically-oriented readers and precluding systematic cross-domain technology maturity comparison. This review addresses that structural gap through a specialty-first synthesis of the quantum-medicine literature and the introduction of an original five-dimension Translational Readiness Taxonomy (TRT: Hardware Maturity, Validation Setting, Benchmarking Rigour, Reproducibility, Regulatory Pathway) as a replicable cross-domain maturity assessment instrument. A total of 168 peer-reviewed studies were identified through a reproducible seven-step PRISMA-aligned screening pipeline from a deduplicated corpus of 14,303 records (Scopus n = 7,081; Web of Science n = 4,644; PubMed n = 2,578), organised across five clinical domains: cardiology , neurology , oncology, genomics and drug discovery , and health data security ; 2022; Indumathi et al. 2024; Roosan et al. 2025; Freyer et al. 2025; Roosan et al. 2024; Lo et al. 2024; Singh et al. . Three original synthesising frameworks are additionally introduced: the Quantum-Medicine Translation Pipeline (QMTP), the Evidence-Maturity Bubble Matrix, and the Quantum-Medicine Ecosystem Map. Neurology leads translational readiness across hardware maturity, clinical validation, and benchmarking dimensions, driven by progressive optically-pumped-magnetometer magnetoencephalography (OPM-MEG) clinical adoption. Oncology, despite constituting 41.7% of included studies and generating the four highest-cited records, exhibits the lowest TRT composite score, with no prospective patient-recruited trials identified across its 70 included studies. A QML-framing-versus-evidence gap is confirmed in cardiology and oncology, where near-term clinical QML claims lack the computational proof-of-concept infrastructure that would logically need to precede them. Health data security leads regulatory maturity via the 2024 NIST finalisation of post-quantum cryptographic standards (CRYSTALS-Kyber/FIPS 203)
Background: The accelerating confluence of quantum computing, big data infrastructures, and advanced artificial intelligence (AI) models is reshaping the theoretical and practical contours of financial analytics, digital marketing, supply chain optimization, and educational infrastructures. Scholarly and practitioner literatures document significant advances across each domain — quantum architectures and algorithmic paradigms, expansive big data platforms and measurement regimes, and powerful AI systems including large language models and multimodal foundation models — yet integrated frameworks that articulate the combined potential and attendant challenges remain nascent (Preskill, 2023; Tosi et al., 2024; Hong et al., 2024). Objectives: This article synthesizes the extant literature to construct a conceptual and methodological scaffold for integrating quantum-enabled computation with big data and AI in financial and economic analysis. It clarifies problem spaces, articulates methodological choices conducive to responsible adoption, analyzes potential transformative outcomes across organizational practice and education, and identifies unresolved research gaps demanding priority attention. The approach emphasizes theoretical rigor, cross-disciplinary citation, and practical interpretive analysis to serve both academic researchers and senior practitioners. Methods: We employ a critical integrative review method that draws on systematic literature syntheses, cross-disciplinary theoretical elaboration, and analytic argumentation. The methodology combines thematic synthesis of provided references, translation of technical claims into descriptive analytical narratives, and normative discussion of implementation pathways supported by cited evidence (Tosi et al., 2024; Sardi et al., 2023). Findings: Quantum computing promises algorithmic speedups for classes of problems relevant to finance (optimization, sampling, and certain linear-algebraic subroutines), yet practical advantage depends on hybrid classical-quantum architectures and careful encoding of big data into quantum-accessible representations (Preskill, 2023; Rayhan & Shahana, 2023). AI advances, particularly in foundation models and multimodal systems, provide powerful representational tools that reshape feature engineering, natural language understanding, and scenario simulation for economic forecasting and M&A diligence (Arachchige et al., 2023; Hong et al., 2024). Big data infrastructures have matured over fifteen years, yet integration challenges — governance, measurement, and sustainable performance — persist and condition how quantum and AI systems can actually be deployed (Tosi et al., 2024; Sardi et al., 2023; Rashid et al., 2024). Conclusions: The transformative potential at the intersection of quantum, big data, and AI is real but contingent. Realizing benefits in finance and related domains requires: (1) methodological advances in encoding and hybrid computation, (2) governance frameworks for data quality and ethical AI, (3) workforce re-skilling oriented to new analyst roles, and (4) empirical research focusing on scaled field deployments and measurable outcomes. We conclude with a detailed research agenda and policy recommendations designed to accelerate responsible innovation while managing risks
The convergence of quantum computing and cloud platforms represents a significant paradigm shift in the landscape of digital services, creating unprecedented opportunities for technological advancement. This article provides an extensive review of this integration, examining its technological underpinnings, the current state of commercial and public-sector services, emerging applications, and the persistent challenges that define its developmental trajectory. By synthesizing recent academic literature, detailed industry reports, and policy analyses, we explore the transition from theoretical concepts to practical, accessible quantum systems. We detail the progress made in the Noisy Intermediate-Scale Quantum (NISQ) era, marked by significant hardware advancements from leaders like IBM and Google, and the development of crucial error mitigation techniques that extend the utility of current processors. Key findings indicate that cloud access is the primary mechanism for democratizing quantum resources, accelerating research, and enabling the exploration of high-value use cases in sectors such as finance, pharmaceuticals, and manufacturing. Furthermore, we investigate the growing adoption by non-profit organizations to tackle complex social and environmental challenges. However, substantial hurdles related to quantum decoherence, hardware scalability, software architecture, and the quantum-classical interface remain. The discussion contextualizes these findings, highlighting the strategic importance of national policies, the industry's push towards fault-tolerant systems, and the long-term vision of hybrid quantum-classical supercomputing. We conclude that while the path to achieving full-scale, fault-tolerant quantum computing is long and complex, its integration with cloud infrastructure is the critical catalyst, paving the way for a new frontier in computational capability and societal problem-solving.